Anonymity assessment system

ABSTRACT

A method, computer system, and a computer program product for assessing anonymity of a dataset is provided. The present invention may include receiving an original dataset and an anonymized dataset. The present invention may also include preparing a testing dataset and a training dataset for a machine learning algorithm based on the received original dataset and anonymized dataset. The present invention may then include training a machine learning model based on the prepared training dataset. The present invention may further include generating an evaluation score based on the trained machine learning model and the prepared testing dataset. The present invention may also include presenting the generated evaluation score to a user.

BACKGROUND

The present invention relates generally to the field of computing, andmore particularly to assessment of anonymization.

Anonymization is a process of removing personally identifiableinformation from a dataset in order to maintain the privacy of theindividuals to whom the data refers. Removing the personalidentification fields, such as name, email, address and social securitynumber may not be sufficient to maintain an acceptable level of privacy,since some individuals may still be identified by cross correlationbetween different fields, discovered by statistical analysis.

SUMMARY

Embodiments of the present invention disclose a method, computer system,and a computer program product for assessing anonymity of a dataset. Thepresent invention may include receiving an original dataset and ananonymized dataset. The present invention may also include preparing atesting dataset and a training dataset for a machine learning algorithmbased on the received original dataset and anonymized dataset. Thepresent invention may then include training a machine learning modelbased on the prepared training dataset. The present invention mayfurther include generating an evaluation score based on the trainedmachine learning model and the prepared testing dataset. The presentinvention may also include presenting the generated evaluation score toa user.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is an operational flowchart illustrating a process for assessmentof anonymization according to at least one embodiment;

FIG. 3 is an exemplary illustration of the dataset processing accordingto at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, methodand program product for assessment of anonymization. As such, thepresent embodiment has the capacity to improve the technical field ofassessing anonymity by making such assessments automatic and optionallyproviding hints about a possible de-anonymization process. The systemmay take as input both an original and anonymized dataset. Thereafter,the system may attempt to automatically de-anonymize the dataset usingsupervised learning techniques. More specifically, the system may learneach anonymized column of the dataset using the original values aslabels for supervised learning techniques. The system can also,optionally, provide a suggestion for further anonymization, depending onthe supervised learning algorithm that is used.

As described previously, anonymization is a process of removingpersonally identifiable information from a dataset (e.g., a columnaroriented database such as a spreadsheet) in order to maintain theprivacy of the individuals to whom the data refers. Removing thepersonal identification fields, such as name, email, address and socialsecurity number may not be sufficient to maintain an acceptable level ofprivacy, since the individuals may still be identified by crosscorrelation between different fields, discovered by statisticalanalysis.

Therefore, it may be advantageous to, among other things, provide anautomatic tool to assess the anonymity quality of an anonymized datasetgiven an original dataset.

According to at least one embodiment, the anonymization assessment maybe comprised of three modules: the data preparation procedure, thesupervised learning algorithm, and the model evaluation. The datapreparation procedure may be given as input both the anonymized and theoriginal datasets. Based on the data preparation procedure's input, thesystem may extract the relevant information and prepare a list ofdatasets for the supervised learning algorithm. The anonymized datasetsmay differ from the original dataset by the removal of a column, thepartial masking of a value, or the performing of a possiblynon-reversible transformation, such as hash or encryption. The columnsof the original dataset which are not removed, masked or hidden in theanonymized dataset may be referred to as features.

The present embodiment may also include a supervised learning algorithmmodule, responsible for training the machine learning models. Themachine learning models may be trained on the datasets which are theresult of the data preparation procedure. Each dataset may bepartitioned into training and testing sets. For each training set, apredefined set of models may be learned in order to predict thepossibility of de-anonymization.

Finally, the present embodiment may include a model evaluation module,which may evaluate the models for each dataset by trying to classify theinstances of a testing dataset using standard machine learning metrics.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and an anonymity assessment program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run ananonymity assessment program 110 b that may interact with a database 114and a communication network 116. The networked computer environment 100may include a plurality of computers 102 and servers 112, only one ofwhich is shown. The communication network 116 may include various typesof communication networks, such as a wide area network (WAN), local areanetwork (LAN), a telecommunication network, a wireless network, a publicswitched network and/or a satellite network. It should be appreciatedthat FIG. 1 provides only an illustration of one implementation and doesnot imply any limitations with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 4,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Platform as a Service (PaaS), orInfrastructure as a Service (IaaS). Server 112 may also be located in acloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud. Client computer 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer, orany type of computing devices capable of running a program, accessing anetwork, and accessing a database 114. According to variousimplementations of the present embodiment, the anonymity assessmentprogram 110 a, 110 b may interact with a database 114 that may beembedded in various storage devices, such as, but not limited to acomputer/mobile device 102, a networked server 112, or a cloud storageservice.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the anonymity assessment program 110 a,110 b (respectively) to automatically assess anonymization, given boththe original and anonymized datasets, by using supervised learningtechniques. It may also, optionally, provide a suggestion for furtheranonymization depending on the supervised learning algorithm that isused by the anonymity assessment system. The anonymity assessment methodis explained in more detail below with respect to FIGS. 2 and 3.

Referring now to FIG. 2, an operational flowchart illustrating theexemplary anonymity assessment process 200 used by the anonymityassessment program 110 a and 110 b according to at least one embodimentis depicted. The anonymity assessment process 200 may include a datapreparation procedure 202, a supervised learning algorithm 204, and amodel evaluation module 206.

The data preparation procedure 202 may take as input from a user boththe anonymized and original datasets at 208. The anonymized and originaldatasets may be inputted into the program in an xml spreadsheet, acolumnar oriented database, or a comma separated string, among otherformats. The anonymized dataset may contain a removed column of theoriginal dataset, a partially masked column of the original dataset, ormay have undergone a potentially non-reversible transformation, such ashash encryption, thereby resulting in a hidden column of the originaldataset.

At 210, the data preparation procedure 202 searches the anonymizeddataset column-by-column to determine which columns have been obfuscatedor anonymized (e.g., removed, hidden or masked) and creates a separatedataset for each column which was removed, hidden or masked in theoriginal dataset. If the data preparation procedure 202 finds that thecolumn contains no data, then the data preparation procedure 202 maydetermine that the column was removed. If the data preparation procedure202 finds that the column contains a special character such as anasterisk, whereby the special character is predefined for use as a mask,then the data preparation procedure 202 may determine the column asmasked. If the data preparation procedure 202 does not determine thatthe column was either removed or masked, then the data preparationprocedure 202 may remove the removed and masked columns from bothdatasets and compare the remaining columns of the anonymized dataset tothose of the original dataset to determine that the column was hidden.If the anonymized and original datasets were not inputted into theprogram in a columnar oriented format, then the string may be searchedfor the headings removed, hidden or masked. According to at least oneother embodiment, the data preparation procedure 202 may search andcompare column-by-column the anonymized dataset to the original datasetto determine which columns have been removed, hidden or masked.According to yet another embodiment, the data preparation procedure 202may provide the user with a graphical user interface (i.e. GUI) in whichthe user may mark which columns have been removed, hidden or masked.

After determining that the column was removed, hidden or masked, thedata preparation procedure 202 may create a separate data structure, forexample an xml spreadsheet, a columnar oriented table or a CSV formattedfile (i.e. a comma-separated value file), for each dataset. For eachremoved column, the data preparation procedure 202 may create a datasetthat consists of features of the original dataset and the originalvalues of the removed column. The data preparation procedure 202 maymark the removed column as Class. For each hidden column, the datapreparation procedure 202 may create a dataset that consists of featuresof the original dataset and the original values of the hidden column.The data preparation procedure 202 may mark the hidden column as Class.For each masked column, the data preparation procedure 202 may create adataset that consists of features of the original dataset, the unmaskedpart of the values of the masked column, and the masked part of theoriginal value of the masked column. The data preparation procedure 202may mark the masked part of the original values as Class.

Then, at 212, the testing and training datasets are created. In doingso, the number of rows in each Class may be counted for each of theremoved, hidden or masked datasets and all rows below a predefinedthreshold may be removed. This may be done to avoid overfitting of thedata, which may occur when the complexity of the machine learningalgorithm prevents the algorithm from making good predictions. Thepredefined threshold, which may be used as a marker to indicate the rowsthat may be removed, may be preconfigured in the program. The removal ofrows below a predefined threshold may be used to partition each of theremoved, hidden or masked datasets into training and testing datasets(e.g., by using the known k-fold cross-validation technique or theconventional validation technique). The training and testing datasetsmay be a subset of the removed, hidden or masked datasets, and may beused together to draw conclusions about the anonymization method, givenknown features of the datasets. If the conventional validation techniqueis used to partition the datasets, the training and testing datasets maybe of unequal size, for example, 80% of the removed, hidden or maskeddataset may be partitioned into the training dataset and 20% of theremoved, hidden or masked dataset may be partitioned into the testingdataset, or 70% of the removed, hidden or masked dataset may bepartitioned into the training dataset and 30% of the removed, hidden ormasked dataset may be partitioned into the testing dataset.

At 214, the data preparation procedure 202 prepares the testing andtraining datasets for the supervised learning algorithm 204, a categoryof machine learning algorithms which is used to learn the method forcreating desired outputs given both the desired outputs and the inputs.In preparing the data for the supervised learning algorithm 204, thedata preparation procedure 202 may standardize diverse data, which mayinclude discretizing continuous values with a predefined method,translating categorical values such as colors into numbers, ornormalizing values. The exact preparation of the data may depend on themachine learning algorithm used and whether that algorithm may functionwith numerical or categorical data. The user may select a machinelearning algorithm to use.

Next, at 216, the supervised learning algorithm 204 trains thepredefined set of machine learning models on the training dataset. Thepredefined set of machine learning models may be chosen by the user ofthe program, and may be trained on the training datasets by findingpatterns in the data which may be used to shape the models. The trainedmachine learning models may be used to create predictions on otherdatasets. For example, the machine learning models may later be run onthe testing datasets to correlate the training and testing datasets, anddetermine the quality of the anonymization. Various machine learningalgorithms may be used, including white box models, which may be usefulin predicting the revealing columns of the dataset (i.e. thoseanonymized columns which have a high correlation to non-anonymizedcolumns, and based on which the classifier may be able to de-anonymizethe anonymized columns).

At 218 of the model evaluation module 206, each dataset will beevaluated using machine learning models. If white box models were usedpreviously at 216, the user may be presented with more comprehensiveinformation about the de-anonymization of the datasets, including whatmay have caused the prediction and what the correlation may be betweenportions of data in the given dataset. The model evaluation module 206may also produce an evaluation score which correlates to the quality ofthe anonymization, and may optionally provide a suggestion for furtheranonymization depending on which supervised learning algorithm 204 isused. For example, some supervised learning algorithms may indicate howthe score is determined, or what part of the data correlates to otherparts of the data in the same dataset. The machine learning models maytry to classify the testing dataset using standard statistical metrics,for example, by calculating the receiver operating characteristic curve,which is used to plot the performance of a classifier system, and bymeasuring the area under the curve.

Referring now to FIG. 3, an exemplary illustration of the datasetprocessing 300 used by the anonymity assessment program 110 a and 110 baccording to at least one embodiment is depicted. As discussedpreviously at 208, an original dataset 302 may be inputted into the datapreparation procedure 202 with the anonymized dataset 304. Based on theinput, the data preparation procedure 202 may extract the relevantinformation and prepare a list of datasets for the supervised learningalgorithm 204.

As illustrated in the dataset processing 300, the data preparationprocedure 202 takes as input an original dataset 302 and an anonymizeddataset 304, as discussed previously at 208. The original dataset 302contains sensitive information in the last three columns, therefore,those columns may be marked removed, hidden, and masked in theanonymized dataset 304.

As discussed previously at 210, the data preparation procedure 202 willcreate a separate dataset for each column in the original dataset 302which was removed, masked, or hidden. For the removed column in theanonymized dataset 304, the data preparation procedure 202 may create aremoved column dataset 306 that consists of features and the originalvalues of the removed column. As shown, the original value of theremoved column (i.e. John Doe) is provided and the column is markedClass in the removed dataset 306.

Likewise, for the hidden column in the anonymized dataset 304, the datapreparation procedure 202 will create a hidden column dataset 308 thatconsists of features and the original values of the hidden column. Thehidden column may then be marked Class. As shown, the original value ofthe hidden column (i.e. john@test.com) is provided and the column ismarked Class in the hidden dataset 308.

Lastly, for the masked column in the anonymized dataset 304, the datapreparation procedure 202 will create a masked column dataset 310 thatconsists of features, the unmasked part of the values of the maskedcolumn and the masked part of the original value of the masked column.The masked part of the original value may then be marked Class in themasked dataset 310. As shown, the dataset contains features of theoriginal dataset 302, the unmasked part of the values contained in themasked column (i.e. 12.34.*.*) and the part of the original datasetwhich was masked in the anonymized dataset 304 (i.e. 56.78). Thereafter,the data preparation procedure 202 may use the removed column dataset306, the hidden column dataset 308, and the masked column dataset 310 tocreate the training and testing datasets as described previously at 212.

It may be appreciated that FIGS. 2 and 3 provide only an illustration ofone embodiment and do not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted embodiment may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 and network server 112 may include respectivesets of internal components 902 a, b and external components 904 a, billustrated in FIG. 4. Each of the sets of internal components 902 a, bincludes one or more processors 906, one or more computer-readable RAMs908, and one or more computer-readable ROMs 910 on one or more buses912, and one or more operating systems 914 and one or morecomputer-readable tangible storage devices 916. The one or moreoperating systems 914, the software program 108 and the anonymityassessment program 110 a in client computer 102, and the anonymityassessment program 110 b in network server 112, may be stored on one ormore computer-readable tangible storage devices 916 for execution by oneor more processors 906 via one or more RAMs 908 (which typically includecache memory). In the embodiment illustrated in FIG. 4, each of thecomputer-readable tangible storage devices 916 is a magnetic diskstorage device of an internal hard drive. Alternatively, each of thecomputer-readable tangible storage devices 916 is a semiconductorstorage device such as ROM 910, EPROM, flash memory or any othercomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 and the anonymity assessment program 110 a and 110 b can bestored on one or more of the respective portable computer-readabletangible storage devices 920, read via the respective R/W drive orinterface 918, and loaded into the respective hard drive 916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless wi-fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 and the anonymity assessment program 110 a inclient computer 102 and the anonymity assessment program 110 b innetwork server computer 112 can be downloaded from an external computer(e.g., server) via a network (for example, the Internet, a local areanetwork or other, wide area network) and respective network adapters orinterfaces 922. From the network adapters (or switch port adaptors) orinterfaces 922, the software program 108 and the anonymity assessmentprogram 110 a in client computer 102 and the anonymity assessmentprogram 110 b in network server computer 112 are loaded into therespective hard drive 916. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926, andcomputer mouse 928. The device drivers 930, R/W drive or interface 918,and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 5 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 1100provided by cloud computing environment 1000 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1102 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1104;RISC (Reduced Instruction Set Computer) architecture based servers 1106;servers 1108; blade servers 1110; storage devices 1112; and networks andnetworking components 1114. In some embodiments, software componentsinclude network application server software 1116 and database software1118.

Virtualization layer 1120 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1122; virtual storage 1124; virtual networks 1126, including virtualprivate networks; virtual applications and operating systems 1128; andvirtual clients 1130.

In one example, management layer 1132 may provide the functionsdescribed below. Resource provisioning 1134 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1136provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1138 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1140provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1142 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1144 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1146; software development and lifecycle management 1148;virtual classroom education delivery 1150; data analytics processing1152; transaction processing 1154; and anonymity assessment 1156. Ananonymity assessment program 110 a, 110 b provides a way toautomatically assess anonymization, given both the original andanonymized datasets, by using supervised learning techniques. It canalso, optionally, provide a suggestion for further anonymization.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for assessing anonymity of a dataset,the method comprising: receiving an original dataset and an anonymizeddataset, wherein the anonymized dataset has undergone a hash encryption;preparing a testing dataset and a training dataset for a supervisedmachine learning algorithm based on the received original dataset andanonymized dataset, by standardizing diverse data, includingdiscretizing continuous values with a predefined method, translatingcategorical data into numerical data, and normalizing values; training amachine learning model based on the prepared training dataset, using anoriginal value as a label for the machine learning model, wherein themachine learning model is a white box model which predicts a revealingcolumn of the anonymized dataset; generating an evaluation score whichcorrelates to a quality of the anonymity of the anonymized dataset basedon the trained machine learning model and both the prepared testing andtraining datasets; and providing a suggestion for further anonymizationto a user, wherein the suggestion is made based on the generatedevaluation score, by indicating how the evaluation score was generatedand identifying any data within the anonymized dataset which correlatesto other data in the same dataset, and wherein the user is furtherprovided with information about the de-anonymization of the anonymizeddataset.
 2. The method of claim 1, further comprising: analyzing theanonymized dataset; determining at least one obfuscated column withinthe analyzed anonymized dataset by providing the user with a graphicaluser interface (GUI) to mark which columns have been obfuscated; andcreating a separate dataset based on the at least one obfuscated column.3. The method of claim 2, further comprising: partitioning the separatedataset based on a predefined threshold using a k-fold cross-validationtechnique, wherein the separate dataset includes a plurality of rows. 4.The method of claim 3, further comprising: determining a training subsetof rows from the plurality of rows, wherein the determined trainingsubset of rows exceed the predefined threshold; and generating thetraining dataset based on the determined training subset of rows.
 5. Themethod of claim 3, further comprising: determining a testing subset ofrows from the plurality of rows, wherein the determined testing subsetof rows is less than or equal to the predefined threshold; andgenerating the testing dataset based on the determined testing subset ofrows.
 6. The method of claim 5, further comprising: preparing thetraining dataset and the testing dataset for the machine learningalgorithm.
 7. The method of claim 6, further comprising: training themachine learning model based on the prepared training dataset.
 8. Acomputer system for assessing anonymity of a dataset, comprising: one ormore processors, one or more computer-readable memories, one or morecomputer-readable tangible storage medium, and program instructionsstored on at least one of the one or more tangible storage medium forexecution by at least one of the one or more processors via at least oneof the one or more memories, wherein the computer system is capable ofperforming a method comprising: receiving an original dataset and ananonymized dataset, wherein the anonymized dataset has undergone a hashencryption; preparing a testing dataset and a training dataset for asupervised machine learning algorithm based on the received originaldataset and anonymized dataset, by standardizing diverse data, includingdiscretizing continuous values with a predefined method, translatingcategorical data into numerical data, and normalizing values; training amachine learning model based on the prepared training dataset, using anoriginal value as a label for the machine learning model, wherein themachine learning model is a white box model which predicts a revealingcolumn of the anonymized dataset; generating an evaluation score whichcorrelates to a quality of the anonymity of the anonymized dataset basedon the trained machine learning model and both the prepared testing andtraining datasets; and providing a suggestion for further anonymizationto a user, wherein the suggestion is made based on the generatedevaluation score, by indicating how the evaluation score was generatedand identifying any data within the anonymized dataset which correlatesto other data in the same dataset, and wherein the user is furtherprovided with information about the de-anonymization of the anonymizeddataset.
 9. The computer system of claim 8, further comprising:analyzing the anonymized dataset; determining at least one obfuscatedcolumn within the analyzed anonymized dataset by providing the user witha graphical user interface (GUI) to mark which columns have beenobfuscated; and creating a separate dataset based on the at least oneobfuscated column.
 10. The computer system of claim 9, furthercomprising: partitioning the separate dataset based on a predefinedthreshold using a k-fold cross-validation technique, wherein theseparate dataset includes a plurality of rows.
 11. The computer systemof claim 10, further comprising: determining a training subset of rowsfrom the plurality of rows, wherein the determined training subset ofrows exceed the predefined threshold; and generating the trainingdataset based on the determined training subset of rows.
 12. Thecomputer system of claim 10, further comprising: determining a testingsubset of rows from the plurality of rows, wherein the determinedtesting subset of rows is less than or equal to the predefinedthreshold; and generating the testing dataset based on the determinedtesting subset of rows.
 13. The computer system of claim 12, furthercomprising: preparing the training dataset and the testing dataset forthe machine learning algorithm.
 14. The computer system of claim 13,further comprising: training the machine learning model based on theprepared training dataset.
 15. A computer program product for assessinganonymity of a dataset, comprising: one or more computer-readablestorage medium and program instructions stored on at least one of theone or more tangible storage medium, the program instructions executableby a processor, the program instructions comprising: programinstructions to receive an original dataset and an anonymized dataset,wherein the anonymized dataset has undergone a hash encryption; programinstructions to prepare a testing dataset and a training dataset for asupervised machine learning algorithm based on the received originaldataset and anonymized dataset, by standardizing diverse data, includingdiscretizing continuous values with a predefined method, translatingcategorical data into numerical data, and normalizing values; programinstructions to train a machine learning model based on the preparedtraining dataset, using an original value as a label for the machinelearning model, wherein the machine learning model is a white box modelwhich predicts a revealing column of the anonymized dataset; programinstructions to generate an evaluation score which correlates to aquality of the anonymity of the anonymized dataset based on the trainedmachine learning model and both the prepared testing and trainingdatasets; and program instructions to provide a suggestion for furtheranonymization to a user, wherein the suggestion is made based on thegenerated evaluation score, by indicating how the evaluation score wasgenerated and identifying any data within the anonymized dataset whichcorrelates to other data in the same dataset, and wherein the user isfurther provided with information about the de-anonymization of theanonymized dataset.
 16. The computer program product of claim 15,further comprising: program instructions to analyze the anonymizeddataset; program instructions to determine at least one obfuscatedcolumn within the analyzed anonymized dataset by providing the user witha graphical user interface (GUI) to mark which columns have beenobfuscated; and program instructions to create a separate dataset basedon the at least one obfuscated column.
 17. The computer program productof claim 16, further comprising: program instructions to partition theseparate dataset based on a predefined threshold using a k-foldcross-validation technique, wherein the separate dataset includes aplurality of rows.
 18. The computer program product of claim 17, furthercomprising: program instructions to determine a training subset of rowsfrom the plurality of rows, wherein the determined training subset ofrows exceed the predefined threshold; and program instructions togenerate the training dataset based on the determined training subset ofrows.
 19. The computer program product of claim 17, further comprising:program instructions to determine a testing subset of rows from theplurality of rows, wherein the determined testing subset of rows is lessthan or equal to the predefined threshold; and program instructions togenerate the testing dataset based on the determined testing subset ofrows.
 20. The computer program product of claim 19, further comprising:program instructions to prepare the training dataset and the testingdataset for the machine learning algorithm.